Background: Coronary heart disease(CHD) is characterized by arterial wall inflammation and matrix degradation. Matrix metalloproteinase(MMP)-22 and-29 and pro-inflammatory cytokine interleukin-18(IL18) are present in ...Background: Coronary heart disease(CHD) is characterized by arterial wall inflammation and matrix degradation. Matrix metalloproteinase(MMP)-22 and-29 and pro-inflammatory cytokine interleukin-18(IL18) are present in human hearts. IL18 may regulate MMP-22 and-29 expression, which may correlate with CHD progression. Methods and results: Immunoblot analysis showed that IL18 induced MMP-22 expression in human aortic smooth muscle cells. The Mann Whitney test from a prospective study of 194 CHD patients and 68 non-CHD controls demonstrated higher plasma levels of IL18, MMP-22 and-29 in CHD patients than in the controls. A logistic regression test suggested that plasma IL18(odds ratio(OR)=1.131, P=0.007), MMP-22(OR=1.213, P=0.040), and MMP-29(OR=1.198, P=0.033) were independent risk factors of CHD. Pearson's correlation test showed that IL18(coefficient(r)=0.214, P=0.045; r=0.246, P=0.031) and MMP-22(r=0.273, P=0.006; r=0.286, P=0.012) were associated with the Gensini score before and after adjusting for potential confounding factors. The multivariate Pearson's correlation test showed that plasma MMP-22 levels correlated positively with high-sensitive-C-reactive protein(hs-CRP)(r=0.167, P=0.023), and MMP-29 levels correlated negatively with triglyceride(r=-0.169, P=0.018). Spearman's correlation test indicated that plasma IL18 levels associated positively with plasma MMP-22(r=0.845, P<0.001) and MMP-29(r=0.548, P<0.001). Conclusions: Our observations suggest that IL18, MMP-22 and-29 serve as biomarkers and independent risk factors of CHD. Increased systemic IL18 in CHD patients may contribute to elevated plasma MMP-22 and-29 levels in these patients.展开更多
Non-negative matrix factorization （NMF） is a popular feature encoding method for image understanding due to its non-negative properties in representation, but the learnt basis images are not always local due to the ...Non-negative matrix factorization （NMF） is a popular feature encoding method for image understanding due to its non-negative properties in representation, but the learnt basis images are not always local due to the lack of explicit constraints in its objective. Various algebraic or geometric local constraints are hence proposed to shape the behaviour of the original NMF. Such constraints are usually rigid in the sense that they have to be specified beforehand instead of learning from the data. In this paper, we propose a flexible spatial constraint method for NMF learning based on factor analysis. Particularly, to learn the local spatial structure of the images, we apply a series of transformations such as orthogonal rotation and thresholding to the factor loading matrix obtained through factor analysis. Then we map the transformed loading matrix into a Laplacian matrix and incorporate this into a max-margin non-negative matrix factorization framework as a penalty term, aiming to learn a representation space which is non-negative, discriminative and localstructure-preserving. We verify the feasibility and effectiveness of the proposed method on several real world datasets with encouraging results.展开更多
基金supported by the University of Science and Technology Innovation Team of Henan(No.14IRTSTHN018)the Science and Technology Talents Team Construction Program of Zhengzhou City Science and Technology Talents(No.131PLJRC670),Chinathe National Institutes of Health(Nos.HL60942 and HL123568),USA
文摘Background: Coronary heart disease(CHD) is characterized by arterial wall inflammation and matrix degradation. Matrix metalloproteinase(MMP)-22 and-29 and pro-inflammatory cytokine interleukin-18(IL18) are present in human hearts. IL18 may regulate MMP-22 and-29 expression, which may correlate with CHD progression. Methods and results: Immunoblot analysis showed that IL18 induced MMP-22 expression in human aortic smooth muscle cells. The Mann Whitney test from a prospective study of 194 CHD patients and 68 non-CHD controls demonstrated higher plasma levels of IL18, MMP-22 and-29 in CHD patients than in the controls. A logistic regression test suggested that plasma IL18(odds ratio(OR)=1.131, P=0.007), MMP-22(OR=1.213, P=0.040), and MMP-29(OR=1.198, P=0.033) were independent risk factors of CHD. Pearson's correlation test showed that IL18(coefficient(r)=0.214, P=0.045; r=0.246, P=0.031) and MMP-22(r=0.273, P=0.006; r=0.286, P=0.012) were associated with the Gensini score before and after adjusting for potential confounding factors. The multivariate Pearson's correlation test showed that plasma MMP-22 levels correlated positively with high-sensitive-C-reactive protein(hs-CRP)(r=0.167, P=0.023), and MMP-29 levels correlated negatively with triglyceride(r=-0.169, P=0.018). Spearman's correlation test indicated that plasma IL18 levels associated positively with plasma MMP-22(r=0.845, P<0.001) and MMP-29(r=0.548, P<0.001). Conclusions: Our observations suggest that IL18, MMP-22 and-29 serve as biomarkers and independent risk factors of CHD. Increased systemic IL18 in CHD patients may contribute to elevated plasma MMP-22 and-29 levels in these patients.
文摘Non-negative matrix factorization （NMF） is a popular feature encoding method for image understanding due to its non-negative properties in representation, but the learnt basis images are not always local due to the lack of explicit constraints in its objective. Various algebraic or geometric local constraints are hence proposed to shape the behaviour of the original NMF. Such constraints are usually rigid in the sense that they have to be specified beforehand instead of learning from the data. In this paper, we propose a flexible spatial constraint method for NMF learning based on factor analysis. Particularly, to learn the local spatial structure of the images, we apply a series of transformations such as orthogonal rotation and thresholding to the factor loading matrix obtained through factor analysis. Then we map the transformed loading matrix into a Laplacian matrix and incorporate this into a max-margin non-negative matrix factorization framework as a penalty term, aiming to learn a representation space which is non-negative, discriminative and localstructure-preserving. We verify the feasibility and effectiveness of the proposed method on several real world datasets with encouraging results.
